Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction

Hany Ferdinando, Tapio Seppänen, Esko Alasaarela

2017

Abstract

Dimensionality reduction (DR) is an important issue in classification and pattern recognition process. Using features with lower dimensionality helps the machine learning algorithms work more efficient. Besides, it also can improve the performance of the system. This paper explores supervised dimensionality reduction, LDA (Linear Discriminant Analysis), NCA (Neighbourhood Components Analysis), and MCML (Maximally Collapsing Metric Learning), in emotion recognition based on ECG signals from the Mahnob-HCI database. It is a 3-class problem of valence and arousal. Features for kNN (k-nearest neighbour) are based on statistical distribution of dominant frequencies after applying a bivariate empirical mode decomposition. The results were validated using 10-fold cross and LOSO (leave-one-subject-out) validations. Among LDA, NCA, and MCML, the NCA outperformed the other methods. The experiments showed that the accuracy for valence was improved from 55.8% to 64.1%, and for arousal from 59.7% to 66.1% using 10-fold cross validation after transforming the features with projection matrices from NCA. For LOSO validation, there is no significant improvement for valence while the improvement for arousal is significant, i.e. from 58.7% to 69.6%.

Paper Citation

in Harvard Style

Ferdinando H., Seppänen T. and Alasaarela E. (2017). Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 112-118. DOI: 10.5220/0006147801120118

in Bibtex Style

@conference{icpram17,
author={Hany Ferdinando and Tapio Seppänen and Esko Alasaarela},
title={Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={112-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006147801120118},
isbn={978-989-758-222-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction
SN - 978-989-758-222-6
AU - Ferdinando H.
AU - Seppänen T.
AU - Alasaarela E.
PY - 2017
SP - 112
EP - 118
DO - 10.5220/0006147801120118